How Big Data is transforming the retail industry [Case Study included]

Is it possible that you’ve been in a situation where you went out to get groceries and realized you forgot to buy something important? For example, what if you order your groceries online but miss out on a few essentials once more.

Wouldn’t it be great if you didn’t have to worry about remembering what all you needed to order and the recommender system took care of everything else?

Big Data Analytics and Artificial Intelligence are playing a significant role in transforming the retail industry and unlocking hidden business potential by providing answers to all of these questions.

For every company, implementing Big Data Analytics and gaining insights is a difficult task to complete. Fortunately, innovative Big Data Analytics platforms and tools such as Apache Hadoop are making our task much easier and more manageable than it was previously.

Large-scale online retailers are benefiting from Apache Hadoop, which enables them to gain valuable business insights from their customers’ data, thereby increasing customer satisfaction and loyalty.

Now, let’s look at some interesting facts about Big Data Analytics in the retail industry:

The Big Data Analytics market in retail was worth $3496.4 million USD in 2018, according to the latest available data. It is anticipated to grow at a compound annual growth rate (CAGR) of 19.2 percent to reach 13299.6 million USD. In the very short period of 2019-2026, the increase will be nearly four times greater.

The implementation of Big Data Analytics in your retail chain, according to Mckinsey, has the potential to increase the operating margin of the retail firm by approximately 60%.

Power of Big Data in Retail Industry

Let’s take a look at some of the most significant benefits of Big Data in the retail industry:

  • Sales in retail establishments should be increased.
  • Make products more popular by increasing demand for them
  • re-awaken customers who are sleeping or cold
  • Customer’s bill should be increased.
  • Customers’ happiness is being improved.
  • Decisions about the layout of the store, the management of employees, and other matters are influenced by data.
  • Store merchandising is being updated.
  • Developing customized discounts and offers for the intended audience
  • Investigating the preferences and purchasing habits of potential customers
  • Concentrating on high-value customers is becoming more important.
  • Customers’ foot traffic should be increased.
  • Pricing strategies that are effective in generating the greatest amount of income
  • Social media is being monitored.
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How Big Data is Used in the Retail Industry is discussed in detail.

1. Price Optimization

Gartner predicts that by 2025, the top ten retailing behemoths will be utilizing real-time pricing.

Big Data Analytics will aid in the achievement of real-time pricing, which will allow in-store prices to be adjusted in real time for customers.

In this section, retailers attempt to determine the impact of changes in the prices of various products. Understanding the impact of price on sales, customers’ purchasing decisions, product selection, and other factors is aided by “what-if” analyses.

Big data analytics assist retailers in determining the optimal price that will increase sales and, as a result, generate the most revenue.

2. Making Strategic Decisions

The Gartner Group predicts that by 2022, Major Business Institutions will harness the power of Big Data Analytics to improve the quality of their business decisions.

Big Data Analytics can be used to make long-term decisions such as where to locate a retail outlet, how various stores are performing, and so on. Big Data Analytics also aids in the formulation of short-term strategic decisions such as promotions/discounts, product merchandising, product display, and other similar activities.

3. Personalizing Customer Experience

The analysis of customer data aids in the customization of discounts and offers for the targeted customers. This information pertains to purchase history, search history, average bill value, frequency of visits to retail establishments, and so forth..

Big data analytics is used to generate customized SMS messages and emails related to promotions and discounts.

Allow me to share with you some important buzzwords in the Retail Analytics domain, such as Market Basket Analytics, Recommender Systems, Clustering and Segmentation, Predictive Analytics, Trend Analysis, and so on.

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Concepts of Big Data in Retail

Let’s look at some Big Data Analytics concepts to get a better understanding of them:

1. Recommender Systems

Amazon’s recommendations engine, which analyzes data from more than 150 million accounts, was responsible for 29 percent of all sales. e-commerce behemoths made enormous profits as a result of this strategy

When you make a purchase from an e-commerce platform or online retail, you will frequently receive recommendations on what other products you should purchase in conjunction with a particular product, as well as recommendations on what other products were purchased by many people while purchasing a particular product on that platform or online retail.

E-commerce giants such as Amazon, Flipkart, Bigbasket, and others are the most common users of recommender systems. As a result of your previous searches and purchases, Amazon uses big data to recommend items for you.

2. Predictive Analytics

According to Forbes Magazine, nearly half of all families purchase their monthly groceries through the online marketplace.

Our credit cards are frequently used to make payments when we are purchasing groceries or any other commodity. However, there has been a significant increase in the number of credit card frauds in recent years.

Predictive analytics enabled Amazon to reduce credit card fraud by 50% in the first six months after implementing the technology. Predictive analytics is heavily used in the fraud detection tools that Amazon employs to combat fraud.

Increased profit margins are the primary goal of every business venture. However, a retailer must make a variety of decisions regarding inventory, such as how much quantity should be ordered from the manufacturer, among other things. What is the current state of the market demand for various products? What is the response of the customer to the various product offerings?

Because of this, retailers must forecast demand based on a variety of factors including market conditions, customer willingness to pay, previous sales, product popularity, and other factors. When it comes to forecasting future demand and growth, predictive analytics comes to the aid of businesses.

3. Operational Analytics and Supply Chain Management

There are a few more questions, such as where to locate a retail store, which stores are performing well, and which stores have the most foot traffic.

Using historical sales data, understanding customer demographics, and replicating best practices across all of your stores can help you make better operational decisions that will result in better results. Apache Hadoop can be used to analyze millions of sales records in order to generate actionable business insights, according to the company.

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Big Basket Case Study

Through the use of data analytics, Bigbasket has grown its customer base from zero to four million in a little more than five years, according to the company.

Features such as “Smart basket” have assisted in more precisely analyzing customer needs and reducing order time from 20-25 minutes to less than 3-4 minutes from 20-25 minutes previously.

The year 2013 marked the beginning of Bigbasket’s analytics domain. The goal was to increase the number of customers by utilizing the insights gained from data mining and, as a result, improve customer retention rates. Customer churn is the most serious problem that any online retail company has to deal with.

Because of data-driven decision making, big basket saw significant increases in customer retention, an increase in the average customer bill value, and the ability to offer targeted coupons and discounts to their customers.

Bigbasket has also been able to grow its customer base from zero to four million in a little more than five years.

Features such as “Smart Basket” analyze your purchasing patterns, search history, previous purchases, and shopping behavior in order to determine whether you buy the same items over and over again at the grocery store. These insights are then used to make recommendations for items to be purchased during the next round of shopping.

This allows you to save time while browsing the products and increases the efficiency of the ordering process.

Conclusion

Incorporating data-driven insights into your operations can help you improve business efficiency while also distinguishing yourself from your competitors. Implementing concepts such as Market Basket Analytics, Predictive Analytics, and Recommender Systems can lead to an increase in customer satisfaction and, as a result, customer retention.

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